# macop.policies.reinforcement — macop v1.0.5 documentation

# Source code for macop.policies.reinforcement

    """Reinforcement learning policy classes implementations for Operator Selection Strategy
    """
    # main imports
    import logging
    import random
    import math
    import numpy as np

    # module imports
    from .base import Policy

    class UCBPolicy(Policy):
        """Upper Confidence Bound (UCB) policy class which is used for applying UCB strategy when selecting and applying operator

        Rather than performing exploration by simply selecting an arbitrary action, chosen with a probability that remains constant,
        the UCB algorithm changes its exploration-exploitation balance as it gathers more knowledge of the environment.
        It moves from being primarily focused on exploration, when actions that have been tried the least are preferred,
        to instead concentrate on exploitation, selecting the action with the highest estimated reward.

        - Resource link: https://banditalgs.com/2016/09/18/the-upper-confidence-bound-algorithm/

        Attributes:
            operators: {[Operator]} -- list of selected operators for the algorithm
            C: {float} -- The second half of the UCB equation adds exploration, with the degree of exploration being controlled by the hyper-parameter `C`.
            exp_rate: {float} -- exploration rate (probability to choose randomly next operator)
            rewards: {[float]} -- list of summed rewards obtained for each operator
            occurrences: {[int]} -- number of use (selected) of each operator

        Example:

        >>> # operators import
        >>> from macop.operators.discrete.crossovers import SimpleCrossover
        >>> from macop.operators.discrete.mutators import SimpleMutation
        >>> # policy import
        >>> from macop.policies.reinforcement import UCBPolicy
        >>> # solution and algorithm
        >>> from macop.solutions.discrete import BinarySolution
        >>> from macop.algorithms.mono import IteratedLocalSearch
        >>> # evaluator import
        >>> from macop.evaluators.discrete.mono import KnapsackEvaluator
        >>> # evaluator initialization (worths objects passed into data)
        >>> worths = [ random.randint(0, 20) for i in range(20) ]
        >>> evaluator = KnapsackEvaluator(data={'worths': worths})
        >>> # validator specification (based on weights of each objects)
        >>> weights = [ random.randint(5, 30) for i in range(20) ]
        >>> validator = lambda solution: True if sum([weights[i] for i, value in enumerate(solution._data) if value == 1]) < 200 else False
        >>> # initializer function with lambda function
        >>> initializer = lambda x=20: BinarySolution.random(x, validator)
        >>> # operators list with crossover and mutation
        >>> operators = [SimpleCrossover(), SimpleMutation()]
        >>> policy = UCBPolicy(operators)
        >>> algo = IteratedLocalSearch(initializer, evaluator, operators, policy, validator, maximise=True, verbose=False)
        >>> policy._occurences
        [0, 0]
        >>> solution = algo.run(100)
        >>> type(solution).__name__
        'BinarySolution'
        >>> policy._occurences # one more due to first evaluation
        [51, 53]
        """
        def __init__(self, operators, C=100., exp_rate=0.5):
            self._operators = operators
            self._rewards = [0. for o in self._operators]
            self._occurences = [0 for o in self._operators]
            self._C = C
            self._exp_rate = exp_rate

        def select(self):
            """Select using Upper Confidence Bound the next operator to use (using acquired rewards)

            Returns:
                {Operator}: the selected operator
            """

            indices = [i for i, o in enumerate(self._occurences) if o == 0]

            # random choice following exploration rate
            if np.random.uniform(0, 1) <= self._exp_rate:

                index = random.choice(range(len(self._operators)))
                return self._operators[index]

            elif len(indices) == 0:

                # if operator have at least be used one time
                ucbValues = []
                nVisits = sum(self._occurences)

                for i in range(len(self._operators)):

                    ucbValue = self._rewards[i] + self._C * math.sqrt(
                        math.log(nVisits) / (self._occurences[i] + 0.1))
                    ucbValues.append(ucbValue)

                return self._operators[ucbValues.index(max(ucbValues))]

            else:
                return self._operators[random.choice(indices)]

        def apply(self, solution):
            """
            Apply specific operator chosen to create new solution, computes its fitness and returns solution

            - fitness improvment is saved as rewards
            - selected operator occurence is also increased

            Args:
                solution: {Solution} -- the solution to use for generating new solution

            Returns:
                {Solution} -- new generated solution
            """

            operator = self.select()

            logging.info("---- Applying %s on %s" %
                         (type(operator).__name__, solution))

            # apply operator on solution
            newSolution = operator.apply(solution)

            # compute fitness of new solution
            newSolution.evaluate(self._algo._evaluator)

            # compute fitness improvment rate
            if self._algo._maximise:
                fir = (newSolution.fitness() -
                       solution.fitness()) / solution.fitness()
            else:
                fir = (solution.fitness() -
                       newSolution.fitness()) / solution.fitness()

            operator_index = self._operators.index(operator)

            if fir > 0:
                self._rewards[operator_index] += fir

            self._occurences[operator_index] += 1

            logging.info("---- Obtaining %s" % (solution))

            return newSolution